Analyzing Newspaper Articles for Text Related data for finding vulnerable Posts over the Internet that are linked to Terrorist activities

2022 ◽  
Vol 16 (1) ◽  
pp. 0-0
Keyword(s):  
Classification Systems ◽  
Natural Languages ◽  
Suggested Approach ◽  
Word Similarity ◽  
Useful Knowledge ◽  
Related Data ◽  
Knowledge Based ◽  
Related Information ◽  
N Gram ◽  
Experimental Findings

One of the most critical activities of revealing terrorism-related information is classifying online documents.The internet provides consumers with a variety of useful knowledge, and the volume of web material is increasingly growing. This makes finding potentially hazardous records incredibly difficult. To define the contents, merely extracting keywords from records is inadequate. Many methods have been studied so far to develop automatic document classification systems, they are mainly computational and knowledge-based approaches. due to the complexities of natural languages, these approaches do not provide sufficient results. To fix this shortcoming, we given approach of structure dependent on the WordNet hierarchy and the frequency of n-gram data that employs word similarity. Using four different queries terms from four different regions, this approach was checked for the NY Times articles that were sampled. Our suggested approach successfully removes background words and phrases from the document recognizes connected to terrorism texts, according to experimental findings.

scholarly journals Learning Social Media Content Optimization: How Can SMEs Draw the Users' Attention on Official WeChat Accounts?

2022 ◽  
Vol 12 ◽  
Author(s):  
Xueyun Zeng ◽  
Xuening Xu ◽  
Yenchun Jim Wu
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Psychological Needs ◽  
Media Content ◽  
Media Usage ◽  
Knowledge Based ◽  
Related Information ◽  
Artificial Intelligence Technology ◽  
Positive Effect ◽  
Content Optimization

Application of artificial intelligence is accelerating the digital transformation of enterprises, and digital content optimization is crucial to take the users' attention in social media usage. The purpose of this work is to demonstrate how social media content reaches and impresses more users. Using a sample of 345 articles released by Chinese small and medium-sized enterprises (SMEs) on their official WeChat accounts, we employ the self-determination theory to analyze the effects of content optimization strategies on social media visibility. It is found that articles with enterprise-related information optimized for content related to users' psychological needs (heart-based content optimization, mind-based content optimization, and knowledge-based content optimization) achieved higher visibility than that of sheer enterprise-related information, whereas the enterprise-related information embedded with material incentive (benefits-based content optimization) brings lower visibility. The results confirm the positive effect of psychological needs on the diffusion of enterprise-related information, and provide guidance for SMEs to apply artificial intelligence technology to social media practice.

scholarly journals Classification Approac to Management of Polythematic Knowledge

Vestnik NSUEM ◽  
2020 ◽  
pp. 224-234
Author(s):  
Yu. V. Cherepova ◽  
L. K. Bobrov ◽  
I. T. Utepbergenov
Keyword(s):  
Classification Systems ◽  
Information Support ◽  
Information Portal ◽  
Knowledge Based ◽  
Knowledge Presentation ◽  
Innovation Activities ◽  
Russian State ◽  
Engineering Information ◽  
The Republic ◽  
Verbal Knowledge

This paper gives a brief description of the being created system of information support for innovation activities in the Republic of Kazakhstan, which is built as an information portal, that provides navigation in the national and global information space through the provision of metadata about information resources, relevant to the user’s task. The corporate knowledge management system is considered as a component of the information infrastructure for supporting innovation. An approach to the management of polythematic knowledge is proposed, envisaging the representation of knowledge, based on the use of classification type languages. In this case, a thematic rubricator is introduced into the ontology model instead of a thesaurus, where each category (rubric) has its own code, name and set of keywords, characterizing its thematic content. The proposed joint use of thematic rubrics of Russian State rubricator of scientific-engineering information and All-Russian institute of scientific and engineering information allows increase the degree of accuracy of the knowledge presentation, as well as take advantage of establishing the associative relations between different classification systems. Along with this, there is maintained the possibility of a verbal knowledge description in terms of keywords, characterizing the content of subject entries and words from the rubrics titles.

scholarly journals How have governments supported citizens stranded abroad due to COVID-19 travel restrictions? A comparative analysis of the financial and health support in eleven countries

2021 ◽  
Author(s):  
Pippa McDermid ◽  
Adam Craig ◽  
Meru Sheel ◽  
Holly Seale
Keyword(s):  
Information Needs ◽  
Design Tool ◽  
Government Support ◽  
Website Design ◽  
Global Public Health ◽  
Related Data ◽  
Related Information ◽  
Travel Restrictions ◽  
Health Support ◽  
Content Accessibility

Abstract Background: In response to the continuing threat of COVID-19, many countries have implemented some form of border restriction. A repercussion of these restrictions has been that some travellers have been stranded abroad unable to return to their country of residence, and in need for government support. Our analysis explores the COVID-19-related information and support options provided by 11 countries to their citizens stranded overseas due to travel restrictions. We also examined the quality (i.e., readability, accessibility, and useability) of the information that was available from selected governments’ web-based resources.Methods: Between June 18 to June 30, 2021, COVID-19-related webpages from 11 countries (Australia, New Zealand, Fiji, Canada, United States of America (USA), United Kingdom (UK), France, Spain, Japan, Singapore, and Thailand) were reviewed and content relating to information and support for citizens stuck overseas analysed. Government assistance-related data from each webpage was extracted and coded for the following themes: travel arrangements, health and wellbeing, finance and accommodation, information needs, and sources. Readability was examined using the Simplified Measure of Gobbledygook (SMOG) and the Flesch Kincaid readability tests; content ‘accessibility’ was measured using the Web Content Accessibility Guidelines (WCAG) Version 2.1; and content ‘usability’ assessed using the usability heuristics for website design tool.Results: Ninety-eight webpages from 34 websites were evaluated. No country assessed covered all themes analysed. Most provided information and some level of support regarding repatriation options; border control and re-entry measures; medical assistance; and traveller registration. Only three countries provided information or support for emergency housing while abroad, and six provided some form of mental health support for their citizens. Our analysis of the quality of COVID-19-related information available on a subset of four countries’ websites found poor readability and multiple accessibility and usability issues.Conclusion: With large variance in the information and services available across the countries analysed, our results highlight gaps, inconsistencies, and potential inequities in support available, and raise issues pertinent to the quality, accessibility, and usability of information. This study will assist policymakers plan and communicate comprehensive support packages for citizens stuck abroad due to the COVID-19 situation and design future efforts to prepare for global public health emergencies.

A Wireless System for Secure Electronic Healthcare Records Management

2013 ◽  
Vol 5 (4) ◽  
pp. 16-32 ◽  
Author(s):  
Petros Belsis ◽  
Christos Skourlas ◽  
Stefanos Gritzalis
Keyword(s):  
Medical Personnel ◽  
Records Management ◽  
Wireless Devices ◽  
Wireless System ◽  
Agent Based ◽  
Related Data ◽  
Related Information ◽  
Efficient Management ◽  
New Directions ◽  
Wireless Computing

Recent advances in wireless computing and in the hardware of wireless devices has opened new directions in many domains; for example in the medical domain the medical personnel in hospitals is able to use wireless devices to gain ubiquitous access to medical related information. However the sensitivity of medical related data poses many challenges in the effort to securely manage these data. In this paper the authors present an agent based architecture for efficient management of medical data. The authors present the security choices and also provide experimental details about the flexibility of our approach.

Expression and Processing of Inductive Queries

2009 ◽  
pp. 518-526
Author(s):  
Edgard Benítez-Guerrero ◽  
Omar Nieva-García
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Pattern Discovery ◽  
Data Preprocessing ◽  
Knowledge Discovery In Databases ◽  
Digital Information ◽  
Interactive Process ◽  
Useful Knowledge ◽  
Knowledge Based ◽  
Computer Based

The vast amounts of digital information stored in databases and other repositories represent a challenge for finding useful knowledge. Traditionalmethods for turning data into knowledge based on manual analysis reach their limits in this context, and for this reason, computer-based methods are needed. Knowledge Discovery in Databases (KDD) is the semi-automatic, nontrivial process of identifying valid, novel, potentially useful, and understandable knowledge (in the form of patterns) in data (Fayyad, Piatetsky-Shapiro, Smyth & Uthurusamy, 1996). KDD is an iterative and interactive process with several steps: understanding the problem domain, data preprocessing, pattern discovery, and pattern evaluation and usage. For discovering patterns, Data Mining (DM) techniques are applied.

scholarly journals Forecasting Economy-Related Data Utilizing Weight-Constrained Recurrent Neural Networks

Algorithms ◽  
2019 ◽  
Vol 12 (4) ◽  
pp. 85 ◽  
Author(s):  
Ioannis E. Livieris
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Training Algorithm ◽  
Economic Data ◽  
Classification Problems ◽  
Useful Knowledge ◽  
Related Data ◽  
Limited Memory ◽  
Proposed Model ◽  
Projection Strategy

During the last few decades, machine learning has constituted a significant tool in extracting useful knowledge from economic data for assisting decision-making. In this work, we evaluate the performance of weight-constrained recurrent neural networks in forecasting economic classification problems. These networks are efficiently trained with a recently-proposed training algorithm, which has two major advantages. Firstly, it exploits the numerical efficiency and very low memory requirements of the limited memory BFGS matrices; secondly, it utilizes a gradient-projection strategy for handling the bounds on the weights. The reported numerical experiments present the classification accuracy of the proposed model, providing empirical evidence that the application of the bounds on the weights of the recurrent neural network provides more stable and reliable learning.

scholarly journals Microbe-set enrichment analysis facilitates functional interpretation of microbiome profiling data

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Yan Kou ◽  
Xiaomin Xu ◽  
Zhengnong Zhu ◽  
Lei Dai ◽  
Yan Tan
Keyword(s):  
Statistical Significance ◽  
Functional Characterization ◽  
Enrichment Analysis ◽  
Underlying Mechanism ◽  
Functional Interpretation ◽  
Useful Knowledge ◽  
Knowledge Based ◽  
Disease Associations ◽  
Differential Abundance Analysis ◽  
Microbiome Profiling

AbstractThe commensal microbiome is known to influence a variety of host phenotypes. Microbiome profiling followed by differential abundance analysis has been established as an effective approach to study the mechanisms of host-microbiome interactions. However, it is challenging to interpret the collective functions of the resultant microbe-sets due to the lack of well-organized functional characterization of commensal microbiome. We developed microbe-set enrichment analysis (MSEA) to enable the functional interpretation of microbe-sets by examining the statistical significance of their overlaps with annotated groups of microbes that share common attributes such as biological function or phylogenetic similarity. We then constructed microbe-set libraries by query PubMed to find microbe-mammalian gene associations and disease associations by parsing the Disbiome database. To demonstrate the utility of our novel MSEA methodology, we carried out three case studies using publicly available curated knowledge resource and microbiome profiling datasets focusing on human diseases. We found MSEA not only yields consistent findings with the original studies, but also recovers insights about disease mechanisms that are supported by the literature. Overall, MSEA is a useful knowledge-based computational approach to interpret the functions of microbes, which can be integrated with microbiome profiling pipelines to help reveal the underlying mechanism of host-microbiome interactions.

Knowledge based classification systems: Basic issues, a toy system and further prospects

1989 ◽  
Vol 16 (3) ◽  
pp. 146-156
Author(s):  
Brigitte Endres-Niggemeyer ◽  
Bettina Schmidt
Keyword(s):  
Classification Systems ◽  
Knowledge Based

scholarly journals Do We Need Neural Models to Explain Human Judgments of Acceptability?

2020 ◽  
Author(s):  
Wang Jing ◽  
M. Alex Kelly ◽  
David Reitter
Keyword(s):  
Word Order ◽  
Native Speakers ◽  
Native Speaker ◽  
Language Models ◽  
Neural Models ◽  
Word Similarity ◽  
Simple Language ◽  
N Gram ◽  
Acceptability Judgments ◽  
Processing Language

Native speakers can judge whether a sentence is an acceptable instance of their language. Acceptability provides a means of evaluating whether computational language models are processing language in a human-like manner. We test the ability of language models, simple language features, and word embeddings to predict native speakers’ judgments of acceptability on English essays written by non-native speakers. We find that much sentence acceptability variance can be captured by a combination of misspellings, word order, and word similarity (r = 0.494). While predictive neural models fit acceptability judgments well (r = 0.527), we find that a 4-gram model is just as good (r = 0.528). Thanks to incorporating misspellings, our 4-gram model surpasses both the previous unsupervised state-of-the art (r = 0.472), and the average native speaker (r = 0.46), demonstrating that acceptability is well captured by n-gram statistics and simple language features.

scholarly journals Integrating Extended Non-destructive Testing in the Life Cycle Management of Bonded Products—Some Perspectives

2021 ◽  
pp. 331-350
Author(s):  
Welchy Leite Cavalcanti ◽  
Elli Moutsompegka ◽  
Konstantinos Tserpes ◽  
Paweł H. Malinowski ◽  
Wiesław M. Ostachowicz ◽  
...  
Keyword(s):  
Life Cycle ◽  
Sensor Arrays ◽  
Product Management ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Related Data ◽  
Related Information ◽  
Post Process ◽  
Structural Parts ◽  
Non Destructive

AbstractIn this chapter, we outline some perspectives on embracing the datasets gathered using Extended Non-destructive Testing (ENDT) during manufacturing or repair process steps within the life cycle of bonded products. Ensuring that the ENDT data and metadata are FAIR, i.e. findable, accessible, interoperable and re-usable, will support the relevant stakeholders in exploiting the contained material-related information far beyond a stop/go decision, while a shorter time-to-information will facilitate a prompter time-to-decision in process and product management. Exploiting the value of ENDT (meta)data will contribute to increased performance by integrating all defined, measured, analyzed and controlled aspects of material transformation across process and company boundaries. This will facilitate the optimization of manufacturing and repair operations, boosting their energy efficiency and productivity. In this regard, some aspects that are currently driving activities in the field of pre-process, in-process and post-process quality assessment will be addressed in the following. Furthermore, some requirements will be contemplated for harmonized and conjoint data transfer ranging from a bonded product’s beginning-of-life through its end-of-life, the customization of stand-alone or linked ENDT tools, and the implementation of sensor arrays and networks in joints, devices and structural parts to gather material-related data during a product’s middle-of-life application phase, thereby fostering structural health monitoring (SHM).

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